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🐳 Docker Notes – Sentiment-Based Product Recommendation System

This document details the Docker-based deployment pipeline built for this project.

🚀 Dockerization independently contributed by Shibani Roychoudhury as part of the Capstone team project.

📁 Folder Structure

Here’s how the project is structured to support containerized deployment:

├── app.py                  # FastAPI app with web + API routes
├── model.py                # Recommendation + sentiment logic
├── Dockerfile              # Docker image definition
├── requirements.txt        # All dependencies
├── Procfile / runtime.txt  # For Railway/Heroku deployment
├── templates/              # HTML UI (Jinja2)
│   └── index.html
├── pickle/                 # Pre-trained models + vectorizers

🔧 What the Docker Image Contains

The image created from this project includes:

  • Python 3.11 base
  • FastAPI + Uvicorn server
  • Your full application code
  • Mounted templates/ and pickle/ folders

⚙️ Build & Run Commands

To run the app locally using Docker:

# Step 1: Clone the repository
git clone https://github.com/helloshibani/Sentiment-Based-Product-Recommendation-Analysis.git
cd Sentiment-Based-Product-Recommendation-Analysis

# Step 2: Build the image
docker build -t sentiment-recommendation-system .

# Step 3: Run the container
docker run -p 8000:8000 sentiment-recommendation-system

The app will be accessible at: http://localhost:8000

📦 Notes on Model Files

The application depends on multiple .pkl files stored inside the pickle/ directory:

Filename Description
user_final_rating.pkl Matrix of user-product ratings
cleaned-data.pkl Final cleaned product reviews
tfidf-vectorizer.pkl TF-IDF model for review text
sentiment-classification-xg-boost-best-tuned.pkl Trained XGBoost classifier

⚠️ These files are not included in this public repo to avoid large file bloat.
To use them:

🛠 Deployment Support

  • Designed for both local use and Railway/Heroku cloud deployment.
  • Procfile and runtime.txt included to enable smooth deployment.

🙌 Credits

This Docker setup was built and tested independently by Shibani Roychoudhury as part of her hands-on DevOps learning.

For feedback, improvements, or collaboration:
📬 Connect with me on LinkedIn